Loading Data

  X   ID Alter Global Overview Cardinal AffiliativeQ_average item3
1 0  365    37    5.6 6.285714      2.5                  5.2     2
2 1 3572    23    5.7 3.142857      1.0                  4.0     2
3 2 3572    23    5.7 3.142857      1.0                  4.0     2
4 3 3572    23    5.7 3.142857      1.0                  4.0     2
5 4 3572    23    5.7 3.142857      1.0                  4.0     2
6 5 3572    23    5.7 3.142857      1.0                  4.0     2
  ContextQ_average remember_action_likert remember_standing_likert
1              3.5               2.714286                 1.107143
2              4.5               2.250000                 1.714286
3              4.5               2.250000                 1.714286
4              4.5               2.250000                 1.714286
5              4.5               2.250000                 1.714286
6              4.5               2.250000                 1.714286
  attractive_action_likert attractive_standing_likert realistic_action_likert
1                 2.214286                   1.178571                3.392857
2                 2.607143                   2.607143                2.964286
3                 2.607143                   2.607143                2.964286
4                 2.607143                   2.607143                2.964286
5                 2.607143                   2.607143                2.964286
6                 2.607143                   2.607143                2.964286
  realistic_standing_likert AbsolutError SignedAngle..180      IQR        RT
1                  1.964286           NA               NA       NA        NA
2                  2.678571    34.308001       -34.308001 52.35318 16.368489
3                  2.678571    83.069424        83.069424 52.35318  7.974188
4                  2.678571     2.210407         2.210407 52.35318  8.858059
5                  2.678571    30.482306       -30.482306 52.35318  6.429150
6                  2.678571     5.407388         5.407388 52.35318  4.768045
  DistanceToParticipant PointingTaskStartingLocations TrialNumber StartPointID
1                    NA                            NA          NA           NA
2              263.1861                            18          19            2
3              297.9136                             7         274           23
4              173.0462                            22         282           24
5              228.5974                            12         293           25
6              273.0915                             9         330           28
  ID_for_StartingPosition avatarID AvatarPresenceCategory Context
1                      NA       NA                               
2                       7        5                Omitted    True
3                      10        5                Present    True
4                       6        5                Present    True
5                       5        5                Present    True
6                       6        5                Omitted    True
  meaningfulBuilding ImageName
1                             
2         Meaningful  05_CmANo
3         Meaningful    05_CmA
4         Meaningful    05_CmA
5         Meaningful    05_CmA
6         Meaningful  05_CmANo

Recoding for effects and formating factors

HumanA$ContextEffectf <-dplyr::recode(HumanA$Context, 
                                      False = -0.5, True= 0.5,
                                      .default = NaN)
HumanA$AvatarPresenceEffectf <-dplyr::recode(HumanA$AvatarPresenceCategory,
                                             Present = -0.5, Omitted= 0.5,
                                             .default = NaN)
HumanA$ContextEffectf <-factor(HumanA$ContextEffectf,levels= c(-0.5, 0.5), 
                               labels=c('Residential', 'Meaningful')) 
HumanA$AvatarPresencef <-factor(HumanA$AvatarPresenceEffectf,
                                levels= c(-0.5, 0.5), 
                                labels=c('Not displayed', 'displayed')) 

Data descriptives

MainVariables <- subset(HumanA, select = c(AbsolutError, RT))
summary(MainVariables)
  AbsolutError             RT        
 Min.   :  0.00986   Min.   : 1.061  
 1st Qu.: 12.43032   1st Qu.: 3.845  
 Median : 33.48641   Median : 6.438  
 Mean   : 48.17395   Mean   : 8.080  
 3rd Qu.: 71.75575   3rd Qu.:10.717  
 Max.   :179.98339   Max.   :29.366  
 NA's   :4           NA's   :4       
summary(HumanA$AvatarPresenceEffectf)
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max.      NA's 
-0.500000 -0.500000  0.500000  0.000426  0.500000  0.500000         4 
df = HumanA[complete.cases(HumanA),]
ggplot(df, aes(x=ContextEffectf,  y=AbsolutError, fill=AvatarPresencef)) + 
  geom_boxplot(notch=TRUE,
        notchwidth = 0.8,
        outlier.colour="red",
        outlier.fill="red",
        outlier.size=0.5)

ggplot(df, aes(x=ContextEffectf,  y=RT, fill=AvatarPresencef)) + 
  geom_boxplot(notch=TRUE,
        notchwidth = 0.8,
        outlier.colour="red",
        outlier.fill="red",
        outlier.size=0.5)

library(dplyr)
TwoFactorTable <- HumanA %>% 
  group_by(ContextEffectf, AvatarPresencef)%>%
  summarise(AccuracyMean = mean(AbsolutError, na.rm = TRUE),
            n=n(),
            AccuracyStandardDev = sd(AbsolutError, na.rm = TRUE),
            RTMean = mean(RT, na.rm = TRUE),
            RTStandardDev = sd(RT, na.rm = TRUE))
`summarise()` has grouped output by 'ContextEffectf'. You can override using
the `.groups` argument.
library(tidyr)

Attaching package: 'tidyr'

The following objects are masked from 'package:Matrix':

    expand, pack, unpack

The following object is masked from 'package:dlookr':

    extract
TwoFactorTableUnite <- TwoFactorTable %>%
  unite("TwoFactor", ContextEffectf:AvatarPresencef, sep= " ", remove = F)
  
TwoFactorTableUnite <-  TwoFactorTableUnite %>%
  mutate( AccuracyStandardError=AccuracyStandardDev/sqrt(n)) %>%  
  mutate( AccuracyStandardIC=AccuracyStandardDev * qt((1-0.05)/2 + .5, n-1)) %>%
  mutate( RTStandardError=RTStandardDev/sqrt(n)) 
Warning: Ignoring unknown aesthetics: linetype

Warning: Ignoring unknown aesthetics: linetype

Checking for the distribution of Absolut error

df = HumanA[complete.cases(HumanA),]
df$AbsolutErrorR <- round(df$AbsolutError, digits = 3)
qqp(df$AbsolutErrorR, "norm")

[1] 2613  968
qqp(df$AbsolutErrorR, "lnorm")

[1] 2613  968

Linear mixed models

Assessing the need for a multilevel model

interceptOnly <-gls(AbsolutError ~ 1, data = df, 
                    method = "ML")
IDrandomInterceptOnly <-lme(AbsolutError ~ 1, data = df,  
                            random =~1|ID,
                            method = "ML")
StartlocationsrandomIntercept <-update(IDrandomInterceptOnly, .~.,   
                              random=~1|ID/PointingTaskStartingLocations,
                              method= "ML")

Including Id and starting position as random effects significantly improves the fit of the model

Absolut Error Models

I am adding one main factor at a time

MeaningfulContext <-update(StartlocationsrandomIntercept, .~. + ContextEffectf)
summary(MeaningfulContext)
Linear mixed-effects model fit by maximum likelihood
  Data: df 
       AIC     BIC    logLik
  90086.83 90122.2 -45038.42

Random effects:
 Formula: ~1 | ID
        (Intercept)
StdDev:    13.63238

 Formula: ~1 | PointingTaskStartingLocations %in% ID
        (Intercept) Residual
StdDev:    13.67445 40.87166

Fixed effects:  AbsolutError ~ ContextEffectf 
                            Value Std.Error   DF   t-value p-value
(Intercept)              50.85647 2.7933018 7984 18.206580  0.0000
ContextEffectfMeaningful -2.51541 0.9064463 7984 -2.775021  0.0055
 Correlation: 
                         (Intr)
ContextEffectfMeaningful -0.162

Standardized Within-Group Residuals:
       Min         Q1        Med         Q3        Max 
-2.1474521 -0.6819886 -0.2313140  0.5171520  3.5682778 

Number of Observations: 8713
Number of Groups: 
                                   ID PointingTaskStartingLocations %in% ID 
                                   26                                   728 
Anova(MeaningfulContext)
Analysis of Deviance Table (Type II tests)

Response: AbsolutError
                Chisq Df Pr(>Chisq)   
ContextEffectf 7.7025  1   0.005514 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Presence <-update(MeaningfulContext, .~. + AvatarPresencef)
summary(Presence)
Linear mixed-effects model fit by maximum likelihood
  Data: df 
       AIC      BIC    logLik
  90087.93 90130.37 -45037.97

Random effects:
 Formula: ~1 | ID
        (Intercept)
StdDev:    13.63276

 Formula: ~1 | PointingTaskStartingLocations %in% ID
        (Intercept) Residual
StdDev:    13.66898 40.87023

Fixed effects:  AbsolutError ~ ContextEffectf + AvatarPresencef 
                            Value Std.Error   DF   t-value p-value
(Intercept)              51.28773 2.8302387 7983 18.121344  0.0000
ContextEffectfMeaningful -2.51960 0.9064670 7983 -2.779579  0.0055
AvatarPresencefdisplayed -0.85750 0.9039836 7983 -0.948584  0.3429
 Correlation: 
                         (Intr) CntxEM
ContextEffectfMeaningful -0.161       
AvatarPresencefdisplayed -0.161  0.005

Standardized Within-Group Residuals:
       Min         Q1        Med         Q3        Max 
-2.1528433 -0.6787017 -0.2308521  0.5148010  3.5789585 

Number of Observations: 8713
Number of Groups: 
                                   ID PointingTaskStartingLocations %in% ID 
                                   26                                   728 
Anova(Presence)
Analysis of Deviance Table (Type II tests)

Response: AbsolutError
                 Chisq Df Pr(>Chisq)   
ContextEffectf  7.7287  1   0.005435 **
AvatarPresencef 0.9001  1   0.342749   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TwofactorInteraction <-update(Presence, .~. + ContextEffectf*AvatarPresencef)
summary(TwofactorInteraction)
Linear mixed-effects model fit by maximum likelihood
  Data: df 
       AIC      BIC    logLik
  90089.62 90139.13 -45037.81

Random effects:
 Formula: ~1 | ID
        (Intercept)
StdDev:    13.62651

 Formula: ~1 | PointingTaskStartingLocations %in% ID
        (Intercept) Residual
StdDev:    13.67257 40.86898

Fixed effects:  AbsolutError ~ ContextEffectf + AvatarPresencef + ContextEffectf:AvatarPresencef 
                                                     Value Std.Error   DF
(Intercept)                                       51.54286  2.865440 7982
ContextEffectfMeaningful                          -3.02942  1.282282 7982
AvatarPresencefdisplayed                          -1.35775  1.268506 7982
ContextEffectfMeaningful:AvatarPresencefdisplayed  1.01262  1.801138 7982
                                                    t-value p-value
(Intercept)                                       17.987767  0.0000
ContextEffectfMeaningful                          -2.362520  0.0182
AvatarPresencefdisplayed                          -1.070352  0.2845
ContextEffectfMeaningful:AvatarPresencefdisplayed  0.562209  0.5740
 Correlation: 
                                                  (Intr) CntxEM AvtrPr
ContextEffectfMeaningful                          -0.224              
AvatarPresencefdisplayed                          -0.224  0.499       
ContextEffectfMeaningful:AvatarPresencefdisplayed  0.158 -0.707 -0.702

Standardized Within-Group Residuals:
       Min         Q1        Med         Q3        Max 
-2.1599916 -0.6810744 -0.2312979  0.5155334  3.5727545 

Number of Observations: 8713
Number of Groups: 
                                   ID PointingTaskStartingLocations %in% ID 
                                   26                                   728 
Anova(TwofactorInteraction)
Analysis of Deviance Table (Type II tests)

Response: AbsolutError
                                Chisq Df Pr(>Chisq)   
ContextEffectf                 7.7287  1   0.005435 **
AvatarPresencef                0.9001  1   0.342765   
ContextEffectf:AvatarPresencef 0.3162  1   0.573886   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise<- emmeans(TwofactorInteraction, pairwise ~ ContextEffectf*AvatarPresencef)
Pairwise
$emmeans
 ContextEffectf AvatarPresencef emmean   SE df lower.CL upper.CL
 Residential    Not displayed     51.5 2.87 25     45.6     57.4
 Meaningful     Not displayed     48.5 2.86 25     42.6     54.4
 Residential    displayed         50.2 2.86 25     44.3     56.1
 Meaningful     displayed         48.2 2.87 25     42.3     54.1

Degrees-of-freedom method: containment 
Confidence level used: 0.95 

$contrasts
 contrast                                             estimate   SE   df
 Residential Not displayed - Meaningful Not displayed    3.029 1.28 7982
 Residential Not displayed - Residential displayed       1.358 1.27 7982
 Residential Not displayed - Meaningful displayed        3.375 1.28 7982
 Meaningful Not displayed - Residential displayed       -1.672 1.28 7982
 Meaningful Not displayed - Meaningful displayed         0.345 1.28 7982
 Residential displayed - Meaningful displayed            2.017 1.27 7982
 t.ratio p.value
   2.363  0.0845
   1.070  0.7077
   2.630  0.0426
  -1.309  0.5573
   0.269  0.9932
   1.584  0.3879

Degrees-of-freedom method: containment 
P value adjustment: tukey method for comparing a family of 4 estimates 
plot(Pairwise[[2]], CIs = TRUE)

library(multcomp);library(multcompView)
Loading required package: mvtnorm
Loading required package: survival
Loading required package: TH.data

Attaching package: 'TH.data'
The following object is masked from 'package:MASS':

    geyser
CLD <- cld(Pairwise,
          alpha=0.05,
          Letters=letters,
          adjust="sidak")
I bet you wanted to call this with just object[[1]] - use '[[]]' or which' if I'm wrong.
See '? emm_list' for more information
ggplot(CLD,
       aes(x     = ContextEffectf,
           y     = emmean,
           group = AvatarPresencef,
           colours = .group)) +

    geom_point(aes(shape=AvatarPresencef, linetype =AvatarPresencef), position=position_dodge(0.3)) +

    geom_errorbar(aes(linetype=AvatarPresencef,
                      ymin  =  lower.CL,
                      ymax  =  upper.CL),
                      position=position_dodge(0.3),
                      width =  0.2,
                      size  =  0.7) +

    theme_bw() +
    theme(axis.title   = element_text(face = "bold"),
          axis.text    = element_text(face = "bold"),
          plot.caption = element_text(hjust = 0)) +

    ylab("Estimated marginal mean\ Absolute angular error") +
    xlab("Location Meaningfulness") +
    ggtitle ("Marginal Means",

             subtitle = "Meaningfulness * Presence") +

                 labs(caption  = paste0( 
                                   "Boxes indicate the EM mean. \n",
                                   "Error bars indicate the 95% ",
                                   "confidence interval of the EM mean. \n"),
                            hjust=0.5) 
Warning: Ignoring unknown aesthetics: linetype

anova(interceptOnly, IDrandomInterceptOnly, StartlocationsrandomIntercept, 
      MeaningfulContext, Presence, TwofactorInteraction )
                              Model df      AIC      BIC    logLik   Test
interceptOnly                     1  2 91151.27 91165.42 -45573.64       
IDrandomInterceptOnly             2  3 90386.56 90407.77 -45190.28 1 vs 2
StartlocationsrandomIntercept     3  4 90092.53 90120.82 -45042.27 2 vs 3
MeaningfulContext                 4  5 90086.83 90122.20 -45038.42 3 vs 4
Presence                          5  6 90087.93 90130.37 -45037.97 4 vs 5
TwofactorInteraction              6  7 90089.62 90139.13 -45037.81 5 vs 6
                               L.Ratio p-value
interceptOnly                                 
IDrandomInterceptOnly         766.7188  <.0001
StartlocationsrandomIntercept 296.0217  <.0001
MeaningfulContext               7.6986  0.0055
Presence                        0.9000  0.3428
TwofactorInteraction            0.3162  0.5739
plot(TwofactorInteraction, which = 1)

plot(MeaningfulContext, which = 1)

GHQ <- glmer(AbsolutError ~  ContextEffectf*AvatarPresencef + (1|ID), data = HumanA,family=gaussian(link = "log"), nAGQ = 25)  
Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
 - Rescale variables?
summary(GHQ)
Generalized linear mixed model fit by maximum likelihood (Adaptive
  Gauss-Hermite Quadrature, nAGQ = 25) [glmerMod]
 Family: gaussian  ( log )
Formula: AbsolutError ~ ContextEffectf * AvatarPresencef + (1 | ID)
   Data: HumanA

     AIC      BIC   logLik deviance df.resid 
16766820 16766863 -8383404 16766808     9378 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.6944 -0.7128 -0.2587  0.4884  3.7236 

Random effects:
 Groups   Name        Variance Std.Dev.
 ID       (Intercept)  238.1   15.43   
 Residual             1786.7   42.27   
Number of obs: 9384, groups:  ID, 28

Fixed effects:
                                                    Estimate Std. Error t value
(Intercept)                                        3.8495417  0.0690074   55.78
ContextEffectfMeaningful                          -0.0439444  0.0005743  -76.51
AvatarPresencefdisplayed                          -0.0209055  0.0005661  -36.93
ContextEffectfMeaningful:AvatarPresencefdisplayed -0.0050108  0.0008215   -6.10
                                                  Pr(>|z|)    
(Intercept)                                        < 2e-16 ***
ContextEffectfMeaningful                           < 2e-16 ***
AvatarPresencefdisplayed                           < 2e-16 ***
ContextEffectfMeaningful:AvatarPresencefdisplayed 1.06e-09 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
            (Intr) CntxEM AvtrPr
CntxtEffctM -0.004              
AvtrPrsncfd -0.004  0.486       
CntxtEfM:AP  0.003 -0.699 -0.690
optimizer (Nelder_Mead) convergence code: 0 (OK)
Model is nearly unidentifiable: very large eigenvalue
 - Rescale variables?
Anova(GHQ)
Analysis of Deviance Table (Type II Wald chisquare tests)

Response: AbsolutError
                                   Chisq Df Pr(>Chisq)    
ContextEffectf                 12764.585  1  < 2.2e-16 ***
AvatarPresencef                 3227.439  1  < 2.2e-16 ***
ContextEffectf:AvatarPresencef    37.204  1  1.064e-09 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(GHQ)
Analysis of Variance Table
                               npar  Sum Sq Mean Sq F value
ContextEffectf                    1 12731.3 12731.3  7.1255
AvatarPresencef                   1  3234.7  3234.7  1.8104
ContextEffectf:AvatarPresencef    1    37.2    37.2  0.0208
emmeans(GHQ, pairwise ~ ContextEffectf:AvatarPresencef, type = "response")
$emmeans
 ContextEffectf AvatarPresencef response   SE  df asymp.LCL asymp.UCL
 Residential    Not displayed       47.0 3.24 Inf      41.0      53.8
 Meaningful     Not displayed       45.0 3.10 Inf      39.3      51.5
 Residential    displayed           46.0 3.17 Inf      40.2      52.7
 Meaningful     displayed           43.8 3.02 Inf      38.3      50.1

Confidence level used: 0.95 
Intervals are back-transformed from the log scale 

$contrasts
 contrast                                              ratio        SE  df null
 Residential Not displayed / Meaningful Not displayed 1.0449 0.0006001 Inf    1
 Residential Not displayed / Residential displayed    1.0211 0.0005781 Inf    1
 Residential Not displayed / Meaningful displayed     1.0724 0.0006250 Inf    1
 Meaningful Not displayed / Residential displayed     0.9772 0.0005652 Inf    1
 Meaningful Not displayed / Meaningful displayed      1.0263 0.0006105 Inf    1
 Residential displayed / Meaningful displayed         1.0502 0.0006168 Inf    1
 z.ratio p.value
  76.513  <.0001
  36.929  <.0001
 119.859  <.0001
 -39.831  <.0001
  43.567  <.0001
  83.348  <.0001

P value adjustment: tukey method for comparing a family of 4 estimates 
Tests are performed on the log scale 
plot(fitted(GHQ), residuals(GHQ), xlab = "Fitted Values", ylab = "Residuals")
abline(h = 0, lty = 2)
lines(smooth.spline(fitted(GHQ), residuals(GHQ)))

interceptOnly <-gls(log(AbsolutError) ~ 1, data = df, 
                    method = "ML")
IDrandomInterceptOnly <-lme(log(AbsolutError) ~ 1, data = df,  
                            random =~1|ID,
                            method = "ML")
StartlocationsrandomIntercept <-update(IDrandomInterceptOnly, .~.,   
                              random=~1|ID/PointingTaskStartingLocations,
                              method= "ML")
MeaningfulContext <-update(StartlocationsrandomIntercept, .~. + ContextEffectf)
Presence <-update(MeaningfulContext, .~. + AvatarPresencef)
TwofactorInteraction <-update(Presence, .~. + ContextEffectf*AvatarPresencef)
summary(TwofactorInteraction)
Linear mixed-effects model fit by maximum likelihood
  Data: df 
       AIC      BIC    logLik
  29477.89 29527.39 -14731.94

Random effects:
 Formula: ~1 | ID
        (Intercept)
StdDev:   0.4164366

 Formula: ~1 | PointingTaskStartingLocations %in% ID
        (Intercept) Residual
StdDev:   0.3017131  1.27877

Fixed effects:  log(AbsolutError) ~ ContextEffectf + AvatarPresencef + ContextEffectf:AvatarPresencef 
                                                      Value  Std.Error   DF
(Intercept)                                        3.376591 0.08706463 7982
ContextEffectfMeaningful                          -0.133322 0.03971070 7982
AvatarPresencefdisplayed                          -0.064008 0.03939877 7982
ContextEffectfMeaningful:AvatarPresencefdisplayed  0.045421 0.05590503 7982
                                                   t-value p-value
(Intercept)                                       38.78258  0.0000
ContextEffectfMeaningful                          -3.35733  0.0008
AvatarPresencefdisplayed                          -1.62461  0.1043
ContextEffectfMeaningful:AvatarPresencefdisplayed  0.81247  0.4165
 Correlation: 
                                                  (Intr) CntxEM AvtrPr
ContextEffectfMeaningful                          -0.229              
AvatarPresencefdisplayed                          -0.229  0.501       
ContextEffectfMeaningful:AvatarPresencefdisplayed  0.162 -0.707 -0.703

Standardized Within-Group Residuals:
       Min         Q1        Med         Q3        Max 
-5.8952344 -0.4486963  0.1935546  0.6817025  2.2375048 

Number of Observations: 8713
Number of Groups: 
                                   ID PointingTaskStartingLocations %in% ID 
                                   26                                   728 
Anova(TwofactorInteraction)
Analysis of Deviance Table (Type II tests)

Response: log(AbsolutError)
                                 Chisq Df Pr(>Chisq)    
ContextEffectf                 15.5081  1  8.215e-05 ***
AvatarPresencef                 2.1956  1     0.1384    
ContextEffectf:AvatarPresencef  0.6604  1     0.4164    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise<- emmeans(TwofactorInteraction, pairwise ~ ContextEffectf*AvatarPresencef, type='response')
Pairwise 
$emmeans
 ContextEffectf AvatarPresencef response   SE df lower.CL upper.CL
 Residential    Not displayed       29.3 2.55 25     24.5     35.0
 Meaningful     Not displayed       25.6 2.23 25     21.4     30.6
 Residential    displayed           27.5 2.39 25     23.0     32.8
 Meaningful     displayed           25.1 2.19 25     21.0     30.1

Degrees-of-freedom method: containment 
Confidence level used: 0.95 
Intervals are back-transformed from the log scale 

$contrasts
 contrast                                             ratio     SE   df null
 Residential Not displayed / Meaningful Not displayed 1.143 0.0454 7982    1
 Residential Not displayed / Residential displayed    1.066 0.0420 7982    1
 Residential Not displayed / Meaningful displayed     1.164 0.0463 7982    1
 Meaningful Not displayed / Residential displayed     0.933 0.0369 7982    1
 Meaningful Not displayed / Meaningful displayed      1.019 0.0405 7982    1
 Residential displayed / Meaningful displayed         1.092 0.0431 7982    1
 t.ratio p.value
   3.357  0.0044
   1.625  0.3647
   3.817  0.0008
  -1.754  0.2957
   0.468  0.9661
   2.225  0.1166

Degrees-of-freedom method: containment 
P value adjustment: tukey method for comparing a family of 4 estimates 
Tests are performed on the log scale 
plot(Pairwise[[2]])

anova(interceptOnly, IDrandomInterceptOnly, StartlocationsrandomIntercept, 
      MeaningfulContext, Presence, TwofactorInteraction )
                              Model df      AIC      BIC    logLik   Test
interceptOnly                     1  2 30340.42 30354.56 -15168.21       
IDrandomInterceptOnly             2  3 29586.02 29607.24 -14790.01 1 vs 2
StartlocationsrandomIntercept     3  4 29490.14 29518.44 -14741.07 2 vs 3
MeaningfulContext                 4  5 29476.74 29512.10 -14733.37 3 vs 4
Presence                          5  6 29476.55 29518.98 -14732.27 4 vs 5
TwofactorInteraction              6  7 29477.89 29527.39 -14731.94 5 vs 6
                               L.Ratio p-value
interceptOnly                                 
IDrandomInterceptOnly         756.3928  <.0001
StartlocationsrandomIntercept  97.8778  <.0001
MeaningfulContext              15.4029  0.0001
Presence                        2.1953  0.1384
TwofactorInteraction            0.6602  0.4165
plot(TwofactorInteraction, which = 1)

model <- glmmPQL(RT ~  ContextEffectf*AvatarPresencef, ~1|ID/PointingTaskStartingLocations,  family = gaussian(link = "log"), data = HumanA, verbose = FALSE)
summary(model)
Linear mixed-effects model fit by maximum likelihood
  Data: HumanA 
  AIC BIC logLik
   NA  NA     NA

Random effects:
 Formula: ~1 | ID
        (Intercept)
StdDev:   0.2231925

 Formula: ~1 | PointingTaskStartingLocations %in% ID
        (Intercept) Residual
StdDev:   0.2224999 4.923456

Variance function:
 Structure: fixed weights
 Formula: ~invwt 
Fixed effects:  RT ~ ContextEffectf * AvatarPresencef 
                                                      Value  Std.Error   DF
(Intercept)                                       2.0548868 0.04477158 8597
ContextEffectfMeaningful                          0.0017300 0.01749219 8597
AvatarPresencefdisplayed                          0.0110505 0.01718929 8597
ContextEffectfMeaningful:AvatarPresencefdisplayed 0.0217719 0.02441735 8597
                                                   t-value p-value
(Intercept)                                       45.89712  0.0000
ContextEffectfMeaningful                           0.09890  0.9212
AvatarPresencefdisplayed                           0.64287  0.5203
ContextEffectfMeaningful:AvatarPresencefdisplayed  0.89166  0.3726
 Correlation: 
                                                  (Intr) CntxEM AvtrPr
ContextEffectfMeaningful                          -0.195              
AvatarPresencefdisplayed                          -0.197  0.499       
ContextEffectfMeaningful:AvatarPresencefdisplayed  0.138 -0.715 -0.700

Standardized Within-Group Residuals:
       Min         Q1        Med         Q3        Max 
-2.2369429 -0.6791802 -0.3101101  0.3731269  4.3832247 

Number of Observations: 9384
Number of Groups: 
                                   ID PointingTaskStartingLocations %in% ID 
                                   28                                   784 

Response Time models

Checking for the distribution of RT

df = HumanA[complete.cases(HumanA),]
df$RTr <- round(df$RT, digits = 3)
qqp(df$RT, "norm")

[1] 8633 8491
qqp(df$RT, "lnorm")

[1] 8633 8491
interceptOnlyt <-gls(log(RT) ~ 1, data = df, 
                    method = "ML")
IDrandomInterceptOnlyt <-lme(log(RT) ~ 1, data = df,  
                            random =~1|ID,
                            method = "ML")
StartlocationsrandomInterceptt <-lme(log(RT) ~ 1, data = df,   
                              random=~1|ID|PointingTaskStartingLocations,
                              method= "ML")
MeaningfulContext <-update(StartlocationsrandomInterceptt, .~. + ContextEffectf)
Presence <-update(MeaningfulContext, .~. + AvatarPresencef)
TwofactorInteraction <-update(Presence, .~. + ContextEffectf*AvatarPresencef)
summary(TwofactorInteraction)
Linear mixed-effects model fit by maximum likelihood
  Data: df 
       AIC      BIC    logLik
  18247.11 18303.69 -9115.555

Random effects:
 Formula: ~1 | ID | PointingTaskStartingLocations
 Structure: General positive-definite, Log-Cholesky parametrization
            StdDev     Corr  
(Intercept) 0.08246629 (Intr)
1 | IDTRUE  0.08246629 -0.587
Residual    0.68713429       

Fixed effects:  log(RT) ~ ContextEffectf + AvatarPresencef + ContextEffectf:AvatarPresencef 
                                                      Value  Std.Error   DF
(Intercept)                                       1.8224232 0.02051489 8682
ContextEffectfMeaningful                          0.0195495 0.02088165 8682
AvatarPresencefdisplayed                          0.0228840 0.02082944 8682
ContextEffectfMeaningful:AvatarPresencefdisplayed 0.0168581 0.02949335 8682
                                                   t-value p-value
(Intercept)                                       88.83418  0.0000
ContextEffectfMeaningful                           0.93620  0.3492
AvatarPresencefdisplayed                           1.09864  0.2720
ContextEffectfMeaningful:AvatarPresencefdisplayed  0.57159  0.5676
 Correlation: 
                                                  (Intr) CntxEM AvtrPr
ContextEffectfMeaningful                          -0.514              
AvatarPresencefdisplayed                          -0.515  0.505       
ContextEffectfMeaningful:AvatarPresencefdisplayed  0.363 -0.707 -0.706

Standardized Within-Group Residuals:
         Min           Q1          Med           Q3          Max 
-2.689589261 -0.748955546 -0.002091011  0.739456135  2.353193766 

Number of Observations: 8713
Number of Groups: 28 
Anova(TwofactorInteraction)
Analysis of Deviance Table (Type II tests)

Response: log(RT)
                                Chisq Df Pr(>Chisq)  
ContextEffectf                 3.5895  1    0.05815 .
AvatarPresencef                4.5065  1    0.03377 *
ContextEffectf:AvatarPresencef 0.3269  1    0.56751  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwiset<- emmeans(TwofactorInteraction, pairwise ~ ContextEffectf*AvatarPresencef, type='response')
Pairwiset 
$emmeans
 ContextEffectf AvatarPresencef response    SE df lower.CL upper.CL
 Residential    Not displayed       6.19 0.127 27     5.93     6.45
 Meaningful     Not displayed       6.31 0.129 27     6.05     6.58
 Residential    displayed           6.33 0.129 27     6.07     6.60
 Meaningful     displayed           6.56 0.135 27     6.29     6.85

Degrees-of-freedom method: containment 
Confidence level used: 0.95 
Intervals are back-transformed from the log scale 

$contrasts
 contrast                                             ratio     SE   df null
 Residential Not displayed / Meaningful Not displayed 0.981 0.0205 8682    1
 Residential Not displayed / Residential displayed    0.977 0.0204 8682    1
 Residential Not displayed / Meaningful displayed     0.942 0.0198 8682    1
 Meaningful Not displayed / Residential displayed     0.997 0.0207 8682    1
 Meaningful Not displayed / Meaningful displayed      0.961 0.0201 8682    1
 Residential displayed / Meaningful displayed         0.964 0.0201 8682    1
 t.ratio p.value
  -0.936  0.7854
  -1.099  0.6904
  -2.823  0.0246
  -0.161  0.9985
  -1.904  0.2265
  -1.745  0.3006

Degrees-of-freedom method: containment 
P value adjustment: tukey method for comparing a family of 4 estimates 
Tests are performed on the log scale 
ref_grid(TwofactorInteraction)
'emmGrid' object with variables:
    ContextEffectf = Residential, Meaningful
    AvatarPresencef = Not displayed, displayed
Transformation: "log" 
plot(Pairwiset[[2]])

anova(interceptOnlyt, IDrandomInterceptOnlyt, StartlocationsrandomInterceptt, 
      MeaningfulContext, Presence, TwofactorInteraction )
                               Model df      AIC      BIC    logLik   Test
interceptOnlyt                     1  2 18303.41 18317.55 -9149.703       
IDrandomInterceptOnlyt             2  3 16981.94 17003.16 -8487.971 1 vs 2
StartlocationsrandomInterceptt     3  5 18249.43 18284.79 -9119.716 2 vs 3
MeaningfulContext                  4  6 18247.94 18290.38 -9117.971 3 vs 4
Presence                           5  7 18245.44 18294.94 -9115.719 4 vs 5
TwofactorInteraction               6  8 18247.11 18303.69 -9115.555 5 vs 6
                                 L.Ratio p-value
interceptOnlyt                                  
IDrandomInterceptOnlyt         1323.4635  <.0001
StartlocationsrandomInterceptt 1263.4883  <.0001
MeaningfulContext                 3.4887  0.0618
Presence                          4.5052  0.0338
TwofactorInteraction              0.3268  0.5676
plot(TwofactorInteraction, which = 1)

CLD <- cld(Pairwiset,
          alpha=0.05,
          Letters=letters,
          adjust="sidak")
I bet you wanted to call this with just object[[1]] - use '[[]]' or which' if I'm wrong.
See '? emm_list' for more information
ggplot(CLD,
       aes(x     = ContextEffectf,
           y     = response,
           group = AvatarPresencef,
           colours = .group)) +

    geom_point(aes(shape=AvatarPresencef, color=AvatarPresencef), position=position_dodge(0.3)) +

    geom_errorbar(aes(color=AvatarPresencef,
                      ymin  =  lower.CL,
                      ymax  =  upper.CL),
                      position=position_dodge(0.3),
                      width =  0.2,
                      size  =  0.7) +

    theme_bw() +
    theme(axis.title   = element_text(face = "bold"),
          axis.text    = element_text(face = "bold"),
          plot.caption = element_text(hjust = 0)) +

    ylab("Estimated marginal mean\ Response Time in Seconds") +
    xlab("Location Meaningfulness") +
    ggtitle ("Marginal Means",

             subtitle = "Meaningfulness * Presence") +

                 labs(caption  = paste0( 
                                   "Boxes indicate the EM mean. \n",
                                   "Error bars indicate the 95% ",
                                   "confidence interval of the EM mean. \n"),
                            hjust=0.5) 

GHQ <- glmer(RT ~  ContextEffectf*AvatarPresencef + (1|ID), data = HumanA,
    family=gaussian(link = "log"), nAGQ = 25)  
summary(GHQ)
Generalized linear mixed model fit by maximum likelihood (Adaptive
  Gauss-Hermite Quadrature, nAGQ = 25) [glmerMod]
 Family: gaussian  ( log )
Formula: RT ~ ContextEffectf * AvatarPresencef + (1 | ID)
   Data: HumanA

      AIC       BIC    logLik  deviance  df.resid 
 262057.8  262100.7 -131022.9  262045.8      9378 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.9259 -0.6951 -0.2851  0.4426  4.0698 

Random effects:
 Groups   Name        Variance Std.Dev.
 ID       (Intercept)  1.581   1.257   
 Residual             27.904   5.282   
Number of obs: 9384, groups:  ID, 28

Fixed effects:
                                                    Estimate Std. Error t value
(Intercept)                                        2.0602890  0.0450614  45.722
ContextEffectfMeaningful                          -0.0155728  0.0035564  -4.379
AvatarPresencefdisplayed                           0.0009492  0.0035211   0.270
ContextEffectfMeaningful:AvatarPresencefdisplayed  0.0337878  0.0049858   6.777
                                                  Pr(>|z|)    
(Intercept)                                        < 2e-16 ***
ContextEffectfMeaningful                          1.19e-05 ***
AvatarPresencefdisplayed                             0.787    
ContextEffectfMeaningful:AvatarPresencefdisplayed 1.23e-11 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
            (Intr) CntxEM AvtrPr
CntxtEffctM -0.040              
AvtrPrsncfd -0.040  0.510       
CntxtEfM:AP  0.028 -0.714 -0.707
Anova(GHQ)
Analysis of Deviance Table (Type II Wald chisquare tests)

Response: RT
                                 Chisq Df Pr(>Chisq)    
ContextEffectf                  0.4341  1       0.51    
AvatarPresencef                51.3014  1  7.922e-13 ***
ContextEffectf:AvatarPresencef 45.9252  1  1.229e-11 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(GHQ)
Analysis of Variance Table
                               npar Sum Sq Mean Sq F value
ContextEffectf                    1  0.358   0.358  0.0128
AvatarPresencef                   1 51.465  51.465  1.8444
ContextEffectf:AvatarPresencef    1 45.999  45.999  1.6485
emmeans(GHQ, pairwise ~ ContextEffectf:AvatarPresencef )
$emmeans
 ContextEffectf AvatarPresencef emmean     SE  df asymp.LCL asymp.UCL
 Residential    Not displayed     2.06 0.0451 Inf      1.97      2.15
 Meaningful     Not displayed     2.04 0.0451 Inf      1.96      2.13
 Residential    displayed         2.06 0.0451 Inf      1.97      2.15
 Meaningful     displayed         2.08 0.0451 Inf      1.99      2.17

Results are given on the log (not the response) scale. 
Confidence level used: 0.95 

$contrasts
 contrast                                              estimate      SE  df
 Residential Not displayed - Meaningful Not displayed  0.015573 0.00356 Inf
 Residential Not displayed - Residential displayed    -0.000949 0.00352 Inf
 Residential Not displayed - Meaningful displayed     -0.019164 0.00354 Inf
 Meaningful Not displayed - Residential displayed     -0.016522 0.00350 Inf
 Meaningful Not displayed - Meaningful displayed      -0.034737 0.00352 Inf
 Residential displayed - Meaningful displayed         -0.018215 0.00349 Inf
 z.ratio p.value
   4.379  0.0001
  -0.270  0.9932
  -5.419  <.0001
  -4.715  <.0001
  -9.856  <.0001
  -5.220  <.0001

Results are given on the log (not the response) scale. 
P value adjustment: tukey method for comparing a family of 4 estimates